Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations12000
Missing cells3444
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory160.0 B

Variable types

Numeric15
Categorical4

Alerts

error_code is highly overall correlated with has_errorHigh correlation
has_error is highly overall correlated with error_codeHigh correlation
irradiance is highly overall correlated with soiled_irradianceHigh correlation
module_temperature is highly overall correlated with temperatureHigh correlation
power_output is highly overall correlated with voltageHigh correlation
soiled_irradiance is highly overall correlated with irradiance and 1 other fieldsHigh correlation
soiling_ratio is highly overall correlated with soiled_irradianceHigh correlation
temperature is highly overall correlated with module_temperatureHigh correlation
voltage is highly overall correlated with power_outputHigh correlation
power_output has 1110 (9.2%) missing values Missing
temp_diff has 1134 (9.4%) missing values Missing
soiled_irradiance has 1200 (10.0%) missing values Missing
temperature has 198 (1.7%) zeros Zeros
maintenance_count has 213 (1.8%) zeros Zeros
voltage has 3080 (25.7%) zeros Zeros
power_output has 2933 (24.4%) zeros Zeros

Reproduction

Analysis started2025-06-08 14:30:31.675733
Analysis finished2025-06-08 14:30:53.288706
Duration21.61 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

temperature
Real number (ℝ)

High correlation  Zeros 

Distinct11204
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.125713
Minimum0
Maximum145.87968
Zeros198
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:53.339707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.5899314
Q117.229078
median24.720345
Q332.702795
95-th percentile44.661416
Maximum145.87968
Range145.87968
Interquartile range (IQR)15.473717

Descriptive statistics

Standard deviation11.977108
Coefficient of variation (CV)0.47668729
Kurtosis5.2563214
Mean25.125713
Median Absolute Deviation (MAD)7.7263986
Skewness0.7359673
Sum301508.56
Variance143.45112
MonotonicityNot monotonic
2025-06-08T20:00:53.443397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.72034535 582
 
4.9%
0 198
 
1.7%
60 19
 
0.2%
34.19487007 1
 
< 0.1%
6.832680196 1
 
< 0.1%
18.63682531 1
 
< 0.1%
33.77693411 1
 
< 0.1%
42.36278696 1
 
< 0.1%
18.06622791 1
 
< 0.1%
30.83367781 1
 
< 0.1%
Other values (11194) 11194
93.3%
ValueCountFrequency (%)
0 198
1.7%
0.04396895376 1
 
< 0.1%
0.08294318597 1
 
< 0.1%
0.09803330564 1
 
< 0.1%
0.1198962214 1
 
< 0.1%
0.1229438636 1
 
< 0.1%
0.1396738754 1
 
< 0.1%
0.1463880019 1
 
< 0.1%
0.1677154218 1
 
< 0.1%
0.1777595994 1
 
< 0.1%
ValueCountFrequency (%)
145.8796772 1
< 0.1%
144.0852751 1
< 0.1%
142.2162839 1
< 0.1%
139.8620902 1
< 0.1%
130.9180882 1
< 0.1%
128.5463195 1
< 0.1%
122.034225 1
< 0.1%
121.7293983 1
< 0.1%
120.5266704 1
< 0.1%
114.5459053 1
< 0.1%

irradiance
Real number (ℝ)

High correlation 

Distinct11386
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean503.47465
Minimum-564.25232
Maximum1420.6274
Zeros0
Zeros (%)0.0%
Negative253
Negative (%)2.1%
Memory size187.5 KiB
2025-06-08T20:00:53.628489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-564.25232
5-th percentile93.106997
Q1344.61234
median499.65473
Q3660.103
95-th percentile911.05729
Maximum1420.6274
Range1984.8797
Interquartile range (IQR)315.49066

Descriptive statistics

Standard deviation244.37583
Coefficient of variation (CV)0.48537862
Kurtosis0.16131043
Mean503.47465
Median Absolute Deviation (MAD)157.57732
Skewness0.0020131864
Sum6041695.8
Variance59719.546
MonotonicityNot monotonic
2025-06-08T20:00:53.755359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
499.6547299 615
 
5.1%
682.8101293 1
 
< 0.1%
468.0191355 1
 
< 0.1%
215.223134 1
 
< 0.1%
430.185818 1
 
< 0.1%
301.4635844 1
 
< 0.1%
567.317921 1
 
< 0.1%
231.7685163 1
 
< 0.1%
688.25754 1
 
< 0.1%
687.6334028 1
 
< 0.1%
Other values (11376) 11376
94.8%
ValueCountFrequency (%)
-564.2523221 1
< 0.1%
-382.6022752 1
< 0.1%
-366.0908469 1
< 0.1%
-346.3781654 1
< 0.1%
-344.4397038 1
< 0.1%
-313.9025086 1
< 0.1%
-310.4182045 1
< 0.1%
-297.036582 1
< 0.1%
-287.9615351 1
< 0.1%
-287.5278876 1
< 0.1%
ValueCountFrequency (%)
1420.627376 1
< 0.1%
1389.090696 1
< 0.1%
1337.150841 1
< 0.1%
1331.868072 1
< 0.1%
1326.374517 1
< 0.1%
1306.498281 1
< 0.1%
1300.581476 1
< 0.1%
1277.75683 1
< 0.1%
1277.648035 1
< 0.1%
1262.730921 1
< 0.1%

humidity
Real number (ℝ)

Distinct11928
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.824307
Minimum0.0095391164
Maximum99.981928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:53.890194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0095391164
5-th percentile4.7266506
Q124.685456
median50.224152
Q374.713704
95-th percentile95.197449
Maximum99.981928
Range99.972389
Interquartile range (IQR)50.028248

Descriptive statistics

Standard deviation28.950806
Coefficient of variation (CV)0.58105788
Kurtosis-1.1966425
Mean49.824307
Median Absolute Deviation (MAD)25.037664
Skewness0.0024024789
Sum597891.68
Variance838.14917
MonotonicityNot monotonic
2025-06-08T20:00:54.053714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.22415155 73
 
0.6%
55.61404978 1
 
< 0.1%
49.04476646 1
 
< 0.1%
8.76157134 1
 
< 0.1%
50.29401576 1
 
< 0.1%
56.37826313 1
 
< 0.1%
27.54841156 1
 
< 0.1%
14.90662479 1
 
< 0.1%
64.6125053 1
 
< 0.1%
90.81542278 1
 
< 0.1%
Other values (11918) 11918
99.3%
ValueCountFrequency (%)
0.009539116431 1
< 0.1%
0.0136231436 1
< 0.1%
0.01445613313 1
< 0.1%
0.01998237764 1
< 0.1%
0.02908181111 1
< 0.1%
0.06131852474 1
< 0.1%
0.07816322606 1
< 0.1%
0.09916449672 1
< 0.1%
0.1008280231 1
< 0.1%
0.1057661272 1
< 0.1%
ValueCountFrequency (%)
99.98192798 1
< 0.1%
99.96748974 1
< 0.1%
99.9619799 1
< 0.1%
99.95266944 1
< 0.1%
99.94875092 1
< 0.1%
99.94502046 1
< 0.1%
99.94053518 1
< 0.1%
99.93749684 1
< 0.1%
99.93407678 1
< 0.1%
99.92881519 1
< 0.1%

panel_age
Real number (ℝ)

Distinct11394
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.474815
Minimum0.013552628
Maximum34.989441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:54.195575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.013552628
5-th percentile1.8317114
Q19.1810737
median17.497731
Q325.73857
95-th percentile33.059093
Maximum34.989441
Range34.975888
Interquartile range (IQR)16.557496

Descriptive statistics

Standard deviation9.8365395
Coefficient of variation (CV)0.56289808
Kurtosis-1.1001829
Mean17.474815
Median Absolute Deviation (MAD)8.2670275
Skewness-0.0065720587
Sum209697.77
Variance96.75751
MonotonicityNot monotonic
2025-06-08T20:00:54.326099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.49773098 607
 
5.1%
2.150406513 1
 
< 0.1%
12.79016726 1
 
< 0.1%
0.08108357168 1
 
< 0.1%
25.56371116 1
 
< 0.1%
5.167165701 1
 
< 0.1%
24.27031925 1
 
< 0.1%
2.537073818 1
 
< 0.1%
12.00559626 1
 
< 0.1%
30.34437084 1
 
< 0.1%
Other values (11384) 11384
94.9%
ValueCountFrequency (%)
0.01355262806 1
< 0.1%
0.01540993646 1
< 0.1%
0.01919298634 1
< 0.1%
0.0202593225 1
< 0.1%
0.02890787299 1
< 0.1%
0.03494323858 1
< 0.1%
0.04283308728 1
< 0.1%
0.04366406883 1
< 0.1%
0.04893163863 1
< 0.1%
0.05943059805 1
< 0.1%
ValueCountFrequency (%)
34.98944092 1
< 0.1%
34.98823547 1
< 0.1%
34.98816312 1
< 0.1%
34.97785481 1
< 0.1%
34.97238975 1
< 0.1%
34.9661775 1
< 0.1%
34.96220307 1
< 0.1%
34.96219321 1
< 0.1%
34.95863499 1
< 0.1%
34.95808418 1
< 0.1%

maintenance_count
Real number (ℝ)

Zeros 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.99225
Minimum0
Maximum13
Zeros213
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:54.417494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9439234
Coefficient of variation (CV)0.48692427
Kurtosis0.42105072
Mean3.99225
Median Absolute Deviation (MAD)1
Skewness0.51520075
Sum47907
Variance3.7788382
MonotonicityNot monotonic
2025-06-08T20:00:54.497296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4 2848
23.7%
3 2199
18.3%
5 1799
15.0%
2 1709
14.2%
6 1181
9.8%
1 817
 
6.8%
7 653
 
5.4%
8 342
 
2.9%
0 213
 
1.8%
9 141
 
1.2%
Other values (4) 98
 
0.8%
ValueCountFrequency (%)
0 213
 
1.8%
1 817
 
6.8%
2 1709
14.2%
3 2199
18.3%
4 2848
23.7%
5 1799
15.0%
6 1181
9.8%
7 653
 
5.4%
8 342
 
2.9%
9 141
 
1.2%
ValueCountFrequency (%)
13 1
 
< 0.1%
12 9
 
0.1%
11 25
 
0.2%
10 63
 
0.5%
9 141
 
1.2%
8 342
 
2.9%
7 653
 
5.4%
6 1181
9.8%
5 1799
15.0%
4 2848
23.7%

soiling_ratio
Real number (ℝ)

High correlation 

Distinct11391
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70032546
Minimum0.40004186
Maximum0.99997599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:54.582785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.40004186
5-th percentile0.43244693
Q10.56099509
median0.69766345
Q30.84246545
95-th percentile0.96690242
Maximum0.99997599
Range0.59993413
Interquartile range (IQR)0.28147036

Descriptive statistics

Standard deviation0.16801533
Coefficient of variation (CV)0.23991035
Kurtosis-1.0994803
Mean0.70032546
Median Absolute Deviation (MAD)0.14055082
Skewness0.007101221
Sum8403.9055
Variance0.028229149
MonotonicityNot monotonic
2025-06-08T20:00:54.685234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6976634526 610
 
5.1%
0.9085552927 1
 
< 0.1%
0.9099036199 1
 
< 0.1%
0.7091422089 1
 
< 0.1%
0.4571752885 1
 
< 0.1%
0.5108346642 1
 
< 0.1%
0.8897651368 1
 
< 0.1%
0.590371655 1
 
< 0.1%
0.6114252657 1
 
< 0.1%
0.5649382276 1
 
< 0.1%
Other values (11381) 11381
94.8%
ValueCountFrequency (%)
0.4000418565 1
< 0.1%
0.4001009102 1
< 0.1%
0.4002470086 1
< 0.1%
0.4003954597 1
< 0.1%
0.4005527624 1
< 0.1%
0.4005754101 1
< 0.1%
0.4007238629 1
< 0.1%
0.4007331267 1
< 0.1%
0.4007848998 1
< 0.1%
0.4008415747 1
< 0.1%
ValueCountFrequency (%)
0.9999759861 1
< 0.1%
0.9998098157 1
< 0.1%
0.9997519577 1
< 0.1%
0.9997444904 1
< 0.1%
0.9995837669 1
< 0.1%
0.9995440547 1
< 0.1%
0.9995120859 1
< 0.1%
0.9994316065 1
< 0.1%
0.9994053975 1
< 0.1%
0.9994017957 1
< 0.1%

voltage
Real number (ℝ)

High correlation  Zeros 

Distinct8375
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.329925
Minimum0
Maximum417.68276
Zeros3080
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:54.798627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12.350138
Q326.194708
95-th percentile47.252904
Maximum417.68276
Range417.68276
Interquartile range (IQR)26.194708

Descriptive statistics

Standard deviation17.999183
Coefficient of variation (CV)1.1022208
Kurtosis70.334554
Mean16.329925
Median Absolute Deviation (MAD)12.350138
Skewness4.5085641
Sum195959.1
Variance323.9706
MonotonicityNot monotonic
2025-06-08T20:00:54.896551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3080
 
25.7%
12.35013818 547
 
4.6%
30.09586704 1
 
< 0.1%
28.42443045 1
 
< 0.1%
7.848038323 1
 
< 0.1%
12.30071688 1
 
< 0.1%
20.35357257 1
 
< 0.1%
0.003959130864 1
 
< 0.1%
29.90275539 1
 
< 0.1%
4.604473209 1
 
< 0.1%
Other values (8365) 8365
69.7%
ValueCountFrequency (%)
0 3080
25.7%
0.00154045986 1
 
< 0.1%
0.003959130864 1
 
< 0.1%
0.005537716947 1
 
< 0.1%
0.02308199848 1
 
< 0.1%
0.02700292411 1
 
< 0.1%
0.03216217923 1
 
< 0.1%
0.04456428168 1
 
< 0.1%
0.04792266636 1
 
< 0.1%
0.05425958465 1
 
< 0.1%
ValueCountFrequency (%)
417.6827632 1
< 0.1%
375.461023 1
< 0.1%
367.480235 1
< 0.1%
338.7765328 1
< 0.1%
298.9909965 1
< 0.1%
282.4084181 1
< 0.1%
270.0612719 1
< 0.1%
244.6032917 1
< 0.1%
241.1674544 1
< 0.1%
139.1915054 1
< 0.1%

current
Real number (ℝ)

Distinct11414
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7008847
Minimum6.4894132 × 10-5
Maximum7.2563907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:54.988239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.4894132 × 10-5
5-th percentile0.16446463
Q10.80986237
median1.5584131
Q32.3998788
95-th percentile3.794549
Maximum7.2563907
Range7.2563258
Interquartile range (IQR)1.5900165

Descriptive statistics

Standard deviation1.1196555
Coefficient of variation (CV)0.6582783
Kurtosis0.17821935
Mean1.7008847
Median Absolute Deviation (MAD)0.79139673
Skewness0.69825086
Sum20410.617
Variance1.2536284
MonotonicityNot monotonic
2025-06-08T20:00:55.085887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.558413082 587
 
4.9%
0.3621093434 1
 
< 0.1%
0.9682846788 1
 
< 0.1%
0.06910124869 1
 
< 0.1%
1.713852 1
 
< 0.1%
1.696936226 1
 
< 0.1%
0.7871881845 1
 
< 0.1%
1.008129062 1
 
< 0.1%
1.215975279 1
 
< 0.1%
0.5618593581 1
 
< 0.1%
Other values (11404) 11404
95.0%
ValueCountFrequency (%)
6.489413229 × 10-51
< 0.1%
0.0003028035065 1
< 0.1%
0.0005223321141 1
< 0.1%
0.0005492417465 1
< 0.1%
0.0005981046183 1
< 0.1%
0.0006067589872 1
< 0.1%
0.0006075573181 1
< 0.1%
0.001397799148 1
< 0.1%
0.001473902221 1
< 0.1%
0.00231534317 1
< 0.1%
ValueCountFrequency (%)
7.256390735 1
< 0.1%
6.507955763 1
< 0.1%
6.324634802 1
< 0.1%
6.260188934 1
< 0.1%
6.001444333 1
< 0.1%
5.998698109 1
< 0.1%
5.974709219 1
< 0.1%
5.862961506 1
< 0.1%
5.838678039 1
< 0.1%
5.83740665 1
< 0.1%

module_temperature
Real number (ℝ)

High correlation 

Distinct11383
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.029415
Minimum0
Maximum65
Zeros17
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:55.264820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.052143
Q121.998419
median29.857669
Q337.844073
95-th percentile50.356806
Maximum65
Range65
Interquartile range (IQR)15.845654

Descriptive statistics

Standard deviation11.918013
Coefficient of variation (CV)0.39687797
Kurtosis-0.13817033
Mean30.029415
Median Absolute Deviation (MAD)7.9230274
Skewness0.084563549
Sum360352.98
Variance142.03904
MonotonicityNot monotonic
2025-06-08T20:00:55.351105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.8576686 580
 
4.8%
65 23
 
0.2%
0 17
 
0.1%
19.51727401 1
 
< 0.1%
37.42144261 1
 
< 0.1%
32.14776341 1
 
< 0.1%
25.73411816 1
 
< 0.1%
14.56375702 1
 
< 0.1%
19.29228782 1
 
< 0.1%
45.96829934 1
 
< 0.1%
Other values (11373) 11373
94.8%
ValueCountFrequency (%)
0 17
0.1%
0.1372598946 1
 
< 0.1%
0.1740890795 1
 
< 0.1%
0.2797311198 1
 
< 0.1%
0.3259752923 1
 
< 0.1%
0.3324826632 1
 
< 0.1%
0.3379009878 1
 
< 0.1%
0.381262328 1
 
< 0.1%
0.3849758042 1
 
< 0.1%
0.5634598879 1
 
< 0.1%
ValueCountFrequency (%)
65 23
0.2%
64.69066347 1
 
< 0.1%
64.67891114 1
 
< 0.1%
64.5767295 1
 
< 0.1%
64.42010575 1
 
< 0.1%
64.41362101 1
 
< 0.1%
64.32004104 1
 
< 0.1%
64.30556662 1
 
< 0.1%
64.30063621 1
 
< 0.1%
64.29978129 1
 
< 0.1%

cloud_coverage
Real number (ℝ)

Distinct11404
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.245595
Minimum0.0010651795
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:55.450443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0010651795
5-th percentile5.5066977
Q126.319099
median49.704133
Q373.875998
95-th percentile94.950786
Maximum1000
Range999.99893
Interquartile range (IQR)47.556899

Descriptive statistics

Standard deviation44.601903
Coefficient of variation (CV)0.87035584
Kurtosis270.44412
Mean51.245595
Median Absolute Deviation (MAD)23.740728
Skewness12.807397
Sum614947.14
Variance1989.3297
MonotonicityNot monotonic
2025-06-08T20:00:55.582631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.70413334 582
 
4.9%
1000 16
 
0.1%
30.48650646 1
 
< 0.1%
52.1856354 1
 
< 0.1%
39.32703109 1
 
< 0.1%
10.57480504 1
 
< 0.1%
6.760446031 1
 
< 0.1%
9.945529656 1
 
< 0.1%
73.89865015 1
 
< 0.1%
33.50988888 1
 
< 0.1%
Other values (11394) 11394
95.0%
ValueCountFrequency (%)
0.001065179489 1
< 0.1%
0.02492664594 1
< 0.1%
0.03338659119 1
< 0.1%
0.03486429448 1
< 0.1%
0.04399147952 1
< 0.1%
0.05630399045 1
< 0.1%
0.061438746 1
< 0.1%
0.06417922874 1
< 0.1%
0.06511704908 1
< 0.1%
0.07635556278 1
< 0.1%
ValueCountFrequency (%)
1000 16
0.1%
99.99565063 1
 
< 0.1%
99.99444358 1
 
< 0.1%
99.9884412 1
 
< 0.1%
99.98733178 1
 
< 0.1%
99.98236537 1
 
< 0.1%
99.97851111 1
 
< 0.1%
99.97330693 1
 
< 0.1%
99.95440172 1
 
< 0.1%
99.94243602 1
 
< 0.1%

wind_speed
Real number (ℝ)

Distinct11920
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4762184
Minimum4.1548467 × 10-5
Maximum14.999029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:55.677783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.1548467 × 10-5
5-th percentile0.73846908
Q13.7856465
median7.4332763
Q311.2167
95-th percentile14.24373
Maximum14.999029
Range14.998987
Interquartile range (IQR)7.4310532

Descriptive statistics

Standard deviation4.3242365
Coefficient of variation (CV)0.5783989
Kurtosis-1.1813613
Mean7.4762184
Median Absolute Deviation (MAD)3.7143144
Skewness0.0024622445
Sum89714.62
Variance18.699021
MonotonicityNot monotonic
2025-06-08T20:00:55.792783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.396090601 81
 
0.7%
7.712644739 1
 
< 0.1%
7.6122409 1
 
< 0.1%
3.252531274 1
 
< 0.1%
2.700604877 1
 
< 0.1%
8.94231174 1
 
< 0.1%
11.45998418 1
 
< 0.1%
4.530717697 1
 
< 0.1%
4.483294962 1
 
< 0.1%
11.31586311 1
 
< 0.1%
Other values (11910) 11910
99.2%
ValueCountFrequency (%)
4.154846745 × 10-51
< 0.1%
0.001347363573 1
< 0.1%
0.002252113866 1
< 0.1%
0.002922468485 1
< 0.1%
0.002931945256 1
< 0.1%
0.004299726733 1
< 0.1%
0.004313223803 1
< 0.1%
0.005103271744 1
< 0.1%
0.005368983927 1
< 0.1%
0.007721626198 1
< 0.1%
ValueCountFrequency (%)
14.9990289 1
< 0.1%
14.99759931 1
< 0.1%
14.99656417 1
< 0.1%
14.99547472 1
< 0.1%
14.99320436 1
< 0.1%
14.99244365 1
< 0.1%
14.99237975 1
< 0.1%
14.99201795 1
< 0.1%
14.99111834 1
< 0.1%
14.98929298 1
< 0.1%

pressure
Real number (ℝ)

Distinct11936
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1013.1206
Minimum974.96741
Maximum1053.6789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:55.944314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum974.96741
5-th percentile996.4696
Q11006.4468
median1013.1943
Q31019.8711
95-th percentile1029.3394
Maximum1053.6789
Range78.711485
Interquartile range (IQR)13.424246

Descriptive statistics

Standard deviation10.013563
Coefficient of variation (CV)0.0098838797
Kurtosis0.051815107
Mean1013.1206
Median Absolute Deviation (MAD)6.7041223
Skewness-0.05481993
Sum12157448
Variance100.27144
MonotonicityNot monotonic
2025-06-08T20:00:56.114828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1012.906121 65
 
0.5%
1010.068901 1
 
< 0.1%
1020.928489 1
 
< 0.1%
1004.806303 1
 
< 0.1%
1025.987693 1
 
< 0.1%
1016.694018 1
 
< 0.1%
1014.887503 1
 
< 0.1%
1018.016056 1
 
< 0.1%
1004.273707 1
 
< 0.1%
1002.616341 1
 
< 0.1%
Other values (11926) 11926
99.4%
ValueCountFrequency (%)
974.9674095 1
< 0.1%
975.8489468 1
< 0.1%
976.2689324 1
< 0.1%
976.7049427 1
< 0.1%
977.2782592 1
< 0.1%
977.5923363 1
< 0.1%
978.1348283 1
< 0.1%
980.2033285 1
< 0.1%
980.2653876 1
< 0.1%
981.0160887 1
< 0.1%
ValueCountFrequency (%)
1053.678894 1
< 0.1%
1049.713708 1
< 0.1%
1048.850112 1
< 0.1%
1047.856107 1
< 0.1%
1046.226443 1
< 0.1%
1045.668315 1
< 0.1%
1045.622479 1
< 0.1%
1045.364559 1
< 0.1%
1044.861444 1
< 0.1%
1044.613786 1
< 0.1%

string_id
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size796.9 KiB
2.0
3025 
0.0
3024 
3.0
3007 
1.0
2944 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 3025
25.2%
0.0 3024
25.2%
3.0 3007
25.1%
1.0 2944
24.5%

Length

2025-06-08T20:00:56.276713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T20:00:56.381762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3025
25.2%
0.0 3024
25.2%
3.0 3007
25.1%
1.0 2944
24.5%

Most occurring characters

ValueCountFrequency (%)
0 15024
41.7%
. 12000
33.3%
2 3025
 
8.4%
3 3007
 
8.4%
1 2944
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15024
41.7%
. 12000
33.3%
2 3025
 
8.4%
3 3007
 
8.4%
1 2944
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15024
41.7%
. 12000
33.3%
2 3025
 
8.4%
3 3007
 
8.4%
1 2944
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15024
41.7%
. 12000
33.3%
2 3025
 
8.4%
3 3007
 
8.4%
1 2944
 
8.2%

error_code
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size796.9 KiB
3.0
3611 
0.0
3568 
1.0
2438 
2.0
2383 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 3611
30.1%
0.0 3568
29.7%
1.0 2438
20.3%
2.0 2383
19.9%

Length

2025-06-08T20:00:56.492286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T20:00:56.583426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 3611
30.1%
0.0 3568
29.7%
1.0 2438
20.3%
2.0 2383
19.9%

Most occurring characters

ValueCountFrequency (%)
0 15568
43.2%
. 12000
33.3%
3 3611
 
10.0%
1 2438
 
6.8%
2 2383
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15568
43.2%
. 12000
33.3%
3 3611
 
10.0%
1 2438
 
6.8%
2 2383
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15568
43.2%
. 12000
33.3%
3 3611
 
10.0%
1 2438
 
6.8%
2 2383
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15568
43.2%
. 12000
33.3%
3 3611
 
10.0%
1 2438
 
6.8%
2 2383
 
6.6%

installation_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size796.9 KiB
1.0
3018 
3.0
3007 
0.0
2996 
2.0
2979 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3018
25.1%
3.0 3007
25.1%
0.0 2996
25.0%
2.0 2979
24.8%

Length

2025-06-08T20:00:56.664451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T20:00:56.727273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3018
25.1%
3.0 3007
25.1%
0.0 2996
25.0%
2.0 2979
24.8%

Most occurring characters

ValueCountFrequency (%)
0 14996
41.7%
. 12000
33.3%
1 3018
 
8.4%
3 3007
 
8.4%
2 2979
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14996
41.7%
. 12000
33.3%
1 3018
 
8.4%
3 3007
 
8.4%
2 2979
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14996
41.7%
. 12000
33.3%
1 3018
 
8.4%
3 3007
 
8.4%
2 2979
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14996
41.7%
. 12000
33.3%
1 3018
 
8.4%
3 3007
 
8.4%
2 2979
 
8.3%

power_output
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct7958
Distinct (%)73.1%
Missing1110
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean30.601732
Minimum0
Maximum1189.3432
Zeros2933
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size187.5 KiB
2025-06-08T20:00:56.813140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12.937726
Q343.853436
95-th percentile118.03725
Maximum1189.3432
Range1189.3432
Interquartile range (IQR)43.853436

Descriptive statistics

Standard deviation46.751088
Coefficient of variation (CV)1.5277268
Kurtosis88.485355
Mean30.601732
Median Absolute Deviation (MAD)12.937726
Skewness5.4688778
Sum333252.86
Variance2185.6642
MonotonicityNot monotonic
2025-06-08T20:00:56.916645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2933
 
24.4%
48.23444573 1
 
< 0.1%
6.177883039 1
 
< 0.1%
22.97305959 1
 
< 0.1%
29.04010129 1
 
< 0.1%
113.6111018 1
 
< 0.1%
31.67688253 1
 
< 0.1%
0.4402023143 1
 
< 0.1%
42.14555591 1
 
< 0.1%
15.82937175 1
 
< 0.1%
Other values (7948) 7948
66.2%
(Missing) 1110
 
9.2%
ValueCountFrequency (%)
0 2933
24.4%
0.0002444744252 1
 
< 0.1%
0.0005083013329 1
 
< 0.1%
0.000784908353 1
 
< 0.1%
0.001021642216 1
 
< 0.1%
0.002187705062 1
 
< 0.1%
0.004246997871 1
 
< 0.1%
0.004834844638 1
 
< 0.1%
0.005452498612 1
 
< 0.1%
0.007242502963 1
 
< 0.1%
ValueCountFrequency (%)
1189.343248 1
< 0.1%
1176.227925 1
< 0.1%
882.1506631 1
< 0.1%
749.2284518 1
< 0.1%
537.3697921 1
< 0.1%
487.8971868 1
< 0.1%
399.559095 1
< 0.1%
363.5324712 1
< 0.1%
348.9788964 1
< 0.1%
320.3484233 1
< 0.1%

temp_diff
Real number (ℝ)

Missing 

Distinct10845
Distinct (%)99.8%
Missing1134
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean4.8890334
Minimum-100.26623
Maximum18.384962
Zeros14
Zeros (%)0.1%
Negative554
Negative (%)4.6%
Memory size187.5 KiB
2025-06-08T20:00:57.137615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-100.26623
5-th percentile-0.024187665
Q12.9260073
median5.0068723
Q37.0597471
95-th percentile9.8933582
Maximum18.384962
Range118.6512
Interquartile range (IQR)4.1337398

Descriptive statistics

Standard deviation4.3764178
Coefficient of variation (CV)0.8951499
Kurtosis272.25896
Mean4.8890334
Median Absolute Deviation (MAD)2.0692257
Skewness-11.974964
Sum53124.237
Variance19.153033
MonotonicityNot monotonic
2025-06-08T20:00:57.264035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
0.1%
5 9
 
0.1%
3.873029444 1
 
< 0.1%
5.136805199 1
 
< 0.1%
1.928045294 1
 
< 0.1%
0.6118203657 1
 
< 0.1%
7.866381019 1
 
< 0.1%
6.116236975 1
 
< 0.1%
1.898894631 1
 
< 0.1%
2.595119137 1
 
< 0.1%
Other values (10835) 10835
90.3%
(Missing) 1134
 
9.4%
ValueCountFrequency (%)
-100.2662341 1
< 0.1%
-99.37269624 1
< 0.1%
-99.03694143 1
< 0.1%
-97.74032613 1
< 0.1%
-95.67861006 1
< 0.1%
-94.37426332 1
< 0.1%
-93.84926465 1
< 0.1%
-91.69815206 1
< 0.1%
-90.72069055 1
< 0.1%
-90.36690776 1
< 0.1%
ValueCountFrequency (%)
18.38496215 1
< 0.1%
16.65839883 1
< 0.1%
15.6627491 1
< 0.1%
15.53878265 1
< 0.1%
15.287211 1
< 0.1%
14.61639819 1
< 0.1%
14.52955543 1
< 0.1%
14.49982245 1
< 0.1%
14.38441061 1
< 0.1%
14.26358817 1
< 0.1%

soiled_irradiance
Real number (ℝ)

High correlation  Missing 

Distinct10800
Distinct (%)100.0%
Missing1200
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean151.61184
Minimum-214.83427
Maximum798.88701
Zeros0
Zeros (%)0.0%
Negative236
Negative (%)2.0%
Memory size187.5 KiB
2025-06-08T20:00:57.408268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-214.83427
5-th percentile5.2568965
Q151.530149
median125.45286
Q3227.2326
95-th percentile391.42111
Maximum798.88701
Range1013.7213
Interquartile range (IQR)175.70245

Descriptive statistics

Standard deviation124.37953
Coefficient of variation (CV)0.82038136
Kurtosis0.67673914
Mean151.61184
Median Absolute Deviation (MAD)82.940523
Skewness0.9335158
Sum1637407.9
Variance15470.267
MonotonicityNot monotonic
2025-06-08T20:00:57.544814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.419551182 1
 
< 0.1%
12.21870861 1
 
< 0.1%
28.0927204 1
 
< 0.1%
61.14471489 1
 
< 0.1%
84.79960893 1
 
< 0.1%
134.304854 1
 
< 0.1%
49.74204958 1
 
< 0.1%
97.68857127 1
 
< 0.1%
352.2722555 1
 
< 0.1%
456.0740724 1
 
< 0.1%
Other values (10790) 10790
89.9%
(Missing) 1200
 
10.0%
ValueCountFrequency (%)
-214.8342665 1
< 0.1%
-211.1066449 1
< 0.1%
-206.5580993 1
< 0.1%
-182.6875571 1
< 0.1%
-170.7284561 1
< 0.1%
-120.4706434 1
< 0.1%
-115.6337505 1
< 0.1%
-111.3451168 1
< 0.1%
-109.5748133 1
< 0.1%
-99.52434694 1
< 0.1%
ValueCountFrequency (%)
798.8870064 1
< 0.1%
772.6209848 1
< 0.1%
726.7603158 1
< 0.1%
677.3187441 1
< 0.1%
669.2515507 1
< 0.1%
664.567934 1
< 0.1%
660.9733216 1
< 0.1%
660.5845395 1
< 0.1%
657.5806862 1
< 0.1%
655.5978235 1
< 0.1%

has_error
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size773.4 KiB
1
8389 
0
3611 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 8389
69.9%
0 3611
30.1%

Length

2025-06-08T20:00:57.649539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T20:00:57.709055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 8389
69.9%
0 3611
30.1%

Most occurring characters

ValueCountFrequency (%)
1 8389
69.9%
0 3611
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8389
69.9%
0 3611
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8389
69.9%
0 3611
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8389
69.9%
0 3611
30.1%

Interactions

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2025-06-08T20:00:40.175760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:41.508056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:42.684181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-08T20:00:44.165012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:45.750257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:46.852803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:47.967047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:49.020076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:50.146167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:51.472813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:52.678581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:33.450444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:34.704007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:35.910129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:37.597485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:39.772164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:41.344169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:42.513238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:44.248621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:45.825543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:46.911511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:48.031946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:49.091998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:50.220416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:51.541543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:52.762014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:33.526442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:34.783821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:35.982766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:37.755671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:39.970998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:41.430138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:42.595890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:44.338381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:45.897584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:46.977351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:48.101822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:49.166337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:50.344711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T20:00:51.612232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-08T20:00:57.777628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
cloud_coveragecurrenterror_codehas_errorhumidityinstallation_typeirradiancemaintenance_countmodule_temperaturepanel_agepower_outputpressuresoiled_irradiancesoiling_ratiostring_idtemp_difftemperaturevoltagewind_speed
cloud_coverage1.000-0.0070.0110.0000.0160.006-0.0100.004-0.0080.004-0.0000.004-0.0070.0020.0110.004-0.003-0.0000.006
current-0.0071.0000.0000.000-0.0080.0000.419-0.018-0.007-0.0020.4570.0060.268-0.0280.000-0.002-0.0030.121-0.001
error_code0.0110.0001.0001.0000.0000.0000.0070.0000.0000.0000.0150.0000.0050.0120.0120.0000.0140.0000.008
has_error0.0000.0001.0001.0000.0200.0000.0110.0000.0000.0070.0120.0000.0120.0260.0270.0000.0000.0000.020
humidity0.016-0.0080.0000.0201.0000.000-0.006-0.019-0.0030.017-0.0180.001-0.004-0.0010.0000.007-0.007-0.0140.003
installation_type0.0060.0000.0000.0000.0001.0000.0090.0000.0090.0130.0000.0050.0180.0060.0170.0000.0000.0130.006
irradiance-0.0100.4190.0070.011-0.0060.0091.000-0.0130.008-0.0180.361-0.0080.591-0.0200.000-0.0020.0090.253-0.014
maintenance_count0.004-0.0180.0000.000-0.0190.000-0.0131.000-0.0100.000-0.009-0.004-0.0170.0060.0000.002-0.009-0.003-0.003
module_temperature-0.008-0.0070.0000.000-0.0030.0090.008-0.0101.000-0.005-0.002-0.0040.012-0.0060.0000.2440.9190.0040.003
panel_age0.004-0.0020.0000.0070.0170.013-0.0180.000-0.0051.000-0.007-0.0080.001-0.0080.000-0.009-0.0040.000-0.003
power_output-0.0000.4570.0150.012-0.0180.0000.361-0.009-0.002-0.0071.000-0.0080.231-0.0180.000-0.006-0.0030.888-0.010
pressure0.0040.0060.0000.0000.0010.005-0.008-0.004-0.004-0.008-0.0081.000-0.010-0.0000.000-0.009-0.004-0.007-0.005
soiled_irradiance-0.0070.2680.0050.012-0.0040.0180.591-0.0170.0120.0010.231-0.0101.000-0.7470.0180.0060.0080.157-0.014
soiling_ratio0.002-0.0280.0120.026-0.0010.006-0.0200.006-0.006-0.008-0.018-0.000-0.7471.0000.000-0.008-0.003-0.0020.019
string_id0.0110.0000.0120.0270.0000.0170.0000.0000.0000.0000.0000.0000.0180.0001.0000.0130.0040.0000.024
temp_diff0.004-0.0020.0000.0000.0070.000-0.0020.0020.244-0.009-0.006-0.0090.006-0.0080.0131.0000.010-0.0070.008
temperature-0.003-0.0030.0140.000-0.0070.0000.009-0.0090.919-0.004-0.003-0.0040.008-0.0030.0040.0101.0000.002-0.001
voltage-0.0000.1210.0000.000-0.0140.0130.253-0.0030.0040.0000.888-0.0070.157-0.0020.000-0.0070.0021.000-0.014
wind_speed0.006-0.0010.0080.0200.0030.006-0.014-0.0030.003-0.003-0.010-0.005-0.0140.0190.0240.008-0.001-0.0141.000

Missing values

2025-06-08T20:00:52.918808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-08T20:00:53.079641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-08T20:00:53.229030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

temperatureirradiancehumiditypanel_agemaintenance_countsoiling_ratiovoltagecurrentmodule_temperaturecloud_coveragewind_speedpressurestring_iderror_codeinstallation_typepower_outputtemp_diffsoiled_irradiancehas_error
id
017.61837985.44983890.81542313.9109636.00.8897656.3703960.06910119.51727433.5098897.1819581034.7824552.01.03.00.4402021.8988959.4195511
134.826323722.80174820.98299320.9165284.00.59037230.0958671.71385237.42144332.3270604.184582992.3197523.00.02.051.5798622.595119296.0800841
233.776934485.49199855.6140501.4469623.00.61142528.4244301.69693632.14776369.6133336.259441999.2134573.01.00.048.234446-1.629171188.6499241
318.584189350.02272049.04476618.8101335.00.6976637.8480380.78718825.73411842.8627602.7696071026.6500782.02.00.06.1778837.149930NaN1
443.044908437.2956228.76157117.4977318.00.56493812.3007171.86762029.85766951.02576311.8469741010.8099431.03.01.022.973060NaN190.2506080
511.760050759.19269750.29401627.2311796.00.95342220.3535731.42678214.56375728.3021037.6400871012.1857210.01.02.029.0401012.80370735.3618361
616.152988682.81012956.37826313.5356592.00.74811512.3501380.36210919.29228871.39758413.3777321018.0860482.03.02.0NaN3.139300171.9893900
743.869417468.01913527.54841227.5849202.00.6976630.0039590.12838745.96829999.2311125.4194921004.9657883.02.03.00.0005082.098882NaN1
89.334953215.22313417.3936888.5844185.00.69766326.5102371.5897848.14686666.02128312.391755997.5506450.03.02.042.145556-1.188087NaN0
929.162480499.65473044.63797124.8979153.00.52439322.7089450.69705437.94123171.7932855.4213031004.4842372.03.03.015.8293728.778751NaN0
temperatureirradiancehumiditypanel_agemaintenance_countsoiling_ratiovoltagecurrentmodule_temperaturecloud_coveragewind_speedpressurestring_iderror_codeinstallation_typepower_outputtemp_diffsoiled_irradiancehas_error
id
1199018.689008-32.76122117.21280530.3443715.00.69766364.0561051.55841320.83230849.7041334.5307181018.0160561.01.02.0NaN2.143301NaN1
1199124.446904671.40416920.89803429.0906134.00.43132921.1209311.73888131.48823528.8546064.4832951004.2737071.03.03.036.7267957.041332381.8079480
119920.735377177.48488254.7723325.3359964.00.63429815.6512821.1321125.06099617.61135311.3158631002.6163412.00.01.017.7190024.32561964.9065151
1199343.877036562.35315619.10080427.4031955.00.80577312.5071703.54023746.90084132.1004677.8226281014.3097001.01.00.044.2783493.023805109.2239701
1199418.587663499.65473027.86384117.4977315.00.67140924.7188240.83600623.61765574.8890604.3161021012.0145172.01.03.020.6650945.029992NaN1
1199540.581656492.44656235.4960124.5081708.00.69766340.8837243.43450548.47605014.0151743.1351281008.9662673.00.03.0140.4153747.894394NaN1
1199616.958524198.8446674.0638164.0212034.00.8109990.0000001.29035223.50265722.7884659.3634241023.5664582.02.03.00.0000006.54413337.5818971
1199724.055333757.6216342.79767015.2539323.00.6976635.8555904.83572931.90837598.19737314.3559451011.6658280.00.02.028.3160487.853042NaN1
1199815.623725177.3762560.67104916.4376132.00.8610870.0000001.15906023.8354959.5811846.9586651017.6572631.03.00.00.0000008.21177024.6398190
1199913.784361687.63340388.4487082.1504073.00.51083538.3724910.96828519.90059873.8986506.1691931029.1560040.03.00.037.1554956.116237336.3664240